Name
Abstract | ||
|---|---|---|
i-vector feature extraction is the state-of-the-art technique for text-independent speaker recognition. There exist studies in literature utilizing i-vector approach for text-dependent speaker verification. However, its performance for Turkish speaker recognition remains unknown. In this study, the performance of i-vector approach is analysed on Turkish text-dependent speaker recognition database consisting of 59 speakers. Experimental results show that, traditional Mel-frequency cepstral coefficients modelled with Gaussian mixture model - universal background model (GMM-UBM) outperforms i-vector system. It is also observed that probabilistic linear discriminant analysis (PLDA) classifier using i-vector features does not bring any performance improvement over the standard cosine distance scoring (CDS) for Turkish text-dependent speaker verification. |
Year | Venue | Keywords |
|---|---|---|
2018 | Signal Processing and Communications Applications Conference | Turkish speaker recognition,i-vector,PLDA |
Field | DocType | ISSN |
Speaker verification,Mel-frequency cepstrum,Turkish,Pattern recognition,Computer science,Feature extraction,Speaker recognition,Artificial intelligence,Classifier (linguistics),Mixture model,Performance improvement | Conference | 2165-0608 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cemal Hanilçi | 1 | 171 | 11.23 |
| Havva Celiktas | 2 | 1 | 0.37 |